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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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This is a link to downhole geophone data collected by Schlumberger. These data were collected in the Utah FORGE deep seismic monitoring wells 58-32 and 56-32. The format is a standard SEGY and the units are bits. To convert to acceleration (m/s2) multiply by 2.333 x 10-7. Use one of the scripts linked below to use wget commands to pull the data.
Here, we describe the simulated sky survey underlying the second data challenge (DC2) carried out in preparation for analysis of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) by the LSST Dark Energy Science Collaboration (LSST DESC). Significant connections across multiple science domains will be a hallmark of LSST; the DC2 program represents a unique modeling effort that stresses this interconnectivity in a way that has not been attempted before. This effort encompasses a full end-to-end approach: starting from a large N-body simulation, through setting up LSST-like observations including realistic cadences, through image simulations, and finally processing with Rubin's LSST Science Pipelines. This last step ensures that we generate data products resembling those to be delivered by the Rubin Observatory as closely as is currently possible. The simulated DC2 sky survey covers six optical bands in a wide-fast-deep area of approximately 300 deg 2 , as well as a deep drilling field of approximately 1 deg 2 . We simulate 5 yr of the planned 10 yr survey. The DC2 sky survey has multiple purposes. First, the LSST DESC working groups can use the data set to develop a range of DESC analysis pipelines to prepare for the advent of actual data. Second, it serves as a realistic test bed for the image processing software under development for LSST by the Rubin Observatory. In particular, simulated data provide a controlled way to investigate certain image-level systematic effects. Finally, the DC2 sky survey enables the exploration of new scientific ideas in both static and time domain cosmology.
Here in this study, we developed a deep learning (DL) framework with a multi-channel three-dimensional convolutional neural network (MC3D-CNN) to predict well performance and thereby assist future environmental remediation design. Such prediction of extraction well performance at designated locations is critical for configuring pump-and-treat (P&T) well network design and operation, setting reasonable target closure dates for overall remedying, and estimating remedy costs. The framework is developed with operational and monitoring data routinely collected during P&T remedy operations, including well extraction and injection rates as well as in situ contaminant concentrations. Traditionally, the collected data were rarely used for purposes other than assessing past well performance and the accuracy of the conceptual site model. However, recent advances in data-driven computational approaches enable better use of the large datasets to inform future well performance, enhance site characterization, and improve remediation planning. In this study, we established a DL framework to integrate transient three-dimensional contaminant plumes and multiple aquifer properties (e.g., hydraulic conductivity and hydrostratigraphic maps) to identify characteristic patterns controlling and representing extraction well mass recovery, aiming at providing future mass recovery estimates for existing wells and candidate wells at any proposed locations. We evaluated our framework by using a realistic synthetic dataset generated from a well-calibrated flow and transport model used in the 200 West Area of the U.S. Department of Energy’s Hanford Site in southeastern Washington state. The multi-channel feature in our framework allows integration of various types and temporal densities of training datasets for DL model development. Overall, we found that the trained DL model achieved an accuracy of over 90% in ranking extraction well performance in validation datasets, and over 80% in predicting high-performance-ranking well locations. This data-informed approach provides a flexible tool to support adaptive site management, streamline decision-making, and potentially reduce remediation time and costs. Our DL framework can be used as a filtering tool to improve the current P&T network optimization design by reducing the number of candidate well locations.
Three types of monitoring are performed for CAU 98: water quality, water level, and institutional control. These are monitored to determine whether the URs remain protective of human health and the environment, and to ensure that the regulatory boundary objectives are being met. Monitoring data will be used in the future, once multiple years of data are available, to evaluate consistency with the groundwater flow and contaminant transport models because the contaminant boundaries calculated with the models are the primary basis of the UR boundaries. Six wells were sampled for water-quality monitoring in 2019. Contaminants of concern were detected only in the two source/plume wells already known to contain contamination as a result of a radionuclide migration experiment. Tritium concentrations in both of these wells, RNM-2S and UE-5n, remain above the Safe Drinking Water Act maximum contaminant level of 20,000 picocuries per liter but declined in 2019 as compared to measurements in 2018. All other contaminants of concern are below the minimum detection level plus analytical error. The water-level monitoring network includes 16 wells. Depth to water measured in 2019 is generally consistent with recent measurements for all wells. Many wells continue to exhibit a long-term downward trend in water level, though changes from 2018 to 2019 are minimal. The sharp 2016 decline in water level in Well ER-5-3-2 remains unexplained, with the lower level persisting through 2019. Rising water-level trends continue to be observed in Well ER-5-3 deep piezometer and former water supply Well WW-5A. Water supply Well WW-5B experienced a rise in water level as a result of an absence of pumping in the first part of the year (due to a mechanical problem), whereas water levels declined in WW-4 and WW-4A in response to greater pumping in 2019. Institutional control monitoring confirmed the URs are recorded in U.S. Department of Energy and U.S. Air Force land management systems, and that no activities within Frenchman Flat basin are occurring that could potentially affect the contaminant boundaries. Survey of groundwater resources in basins surrounding Frenchman Flat similarly identify no current or pending development that would indicate the need to increase monitoring activities or would otherwise cause concern for the closure decision. The URs continue to prevent exposure of the public, workers, and the environment to contaminants of concern by preventing use of potentially contaminated groundwater.
Pervasive cirrus clouds in the upper troposphere and tropical tropopause layer (TTL) influence the climate by altering the top-of-atmosphere radiation balance and stratospheric water vapor budget. These cirrus are often associated with deep convection, which global climate models must parameterize and struggle to accurately simulate. By comparing high-resolution global storm-resolving models from the Dynamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) intercomparison that explicitly simulate deep convection to satellite observations, we assess how well these models simulate deep convection, convectively generated cirrus, and deep convective injection of water into the TTL over representative tropical land and ocean regions. The DYAMOND models simulate deep convective precipitation, organization, and cloud structure fairly well over land and ocean regions, but with clear intermodel differences. All models produce frequent overshooting convection whose strongest updrafts humidify the TTL and are its main source of frozen water. Intermodel differences in cloud properties and convective injection exceed differences between land and ocean regions in each model. We argue that, with further improvements, global storm-resolving models can better represent tropical cirrus and deep convection in present and future climates than coarser-resolution climate models. To realize this potential, they must use available observations to perfect their ice microphysics and dynamical flow solvers.
Ever deeper wells are drilled worldwide to pump potable groundwater. Recent studies argue that overpumping compresses clays and releases reactive dissolved organic carbon (DOC), which in turn drives a series of microbial reactions that affect groundwater potability including arsenic concentrations. Here, we use a novel method to measure the radiocarbon ages of microbial RNA to determine the source of reactive or metabolizable DOC and argue against the compression of clays as the sole source of carbon. We show that microbial RNA (5,230; 5,550; 6,250 yr; n = 3 wells), DOC (280-10,800 yr; n = 13), and methane (modern to 6,240 yr; n = 3), from an overpumped deep aquifer in Bangladesh are much younger than the overlying clay layers deposited during the Pleistocene over 12,000 years ago. Mass-balance indicates that at least half of the carbon incorporated into RNA has to come from reactive DOC or methane that is advected downward via vertical recharge. This metabolism of advected organic carbon could have implications for the quality of water pumped from deep aquifers.
An important technique used by climate modelers to isolate the impacts of increasing greenhouse gasses on Earth System processes is to simulate the impact of an abrupt increase in carbon dioxide. The spatial pattern of change provides a “fingerprint” that is generally much larger than natural variability. Insofar as the response to radiative forcing is linear (the impact of quadrupling CO 2 is twice the impact of doubling CO 2 ) this fingerprint can then be used to estimate the impact of historical greenhouse gas forcing. However, the degree to which biogeochemical cycles respond linearly to radiative forcing has rarely been tested. In this paper, we evaluate which ocean biogeochemical fields are likely to respond linearly to changing radiative forcing, which ones do not, and where linearity breaks down. We also demonstrate that the representation of lateral mixing by mesoscale eddies, which varies significantly across climate models, plays an important role in modulating the breakdown of linearity. Globally integrated surface rates of biogeochemical cycling (primary productivity, particulate export) respond in a relatively linear fashion and are only moderately sensitive to mixing. By contrast, the habitability of the interior ocean (as determined by hypoxia and calcite supersaturation) behaves non-linearly and is very sensitive to mixing. This is because the deep ocean, as well as certain regions in the surface ocean, are very sensitive to the magnitude of deep wintertime convection. The cessation of convection under global warming is strongly modulated by the representation of eddy mixing.
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The computation of dense matrix-matrix products (GEMMs) is central to many modeling and simulation workloads as well as AI/ML deep learning campaigns. In fact, millions of dollars are spent annually on computing GEMMs, and large model training demands are increasing exponentially. Specialized processors such as GPUs are designed to perform well for these operations. However, the performance of GEMMs on GPUs can exhibit complex behaviors depending on many factors, making it challenging to optimize the performance of GEMMs on these processors. In this study we undertake an examination of GEMM performance on several leading GPU models taken from product lines of GPUs to be deployed in forthcoming exascale computing systems. We show results to illustrate the many factors that can affect performance of GEMMs on GPUs. We then present data collected from a large number of test runs for an example GEMM operation to show the dependence behaviors of GEMM rate on matrix dimensions. Finally, we show results from machine learning-based performance models using novel feature engineering methods to fit the measured performance, providing a potential basis for GEMM performance tuning and autotuning methods for GPUs. Recommendations are also given for how to achieve high GEMM performance on modern GPUs.
The DEEPEN (DE-risking Exploration for geothermal Plays in magmatic ENvironments) project was a multi-laboratory, international effort to reduce uncertainty and improve resource characterization in superhot geothermal systems. Building on this foundation, this work advances open-source tools designed to lower the exploration risk and cost of superhot geothermal projects while promoting transparency, reproducibility, and efficiency in exploration workflows. These tools are being tested at two key sites: (1) the Nesjavellir Geothermal Area in Iceland, where the Icelandic Deep Drilling Project (IDDP) will drill its third well, and (2) Newberry Volcano in Oregon, USA, where Mazama Energy will pilot the first superhot enhanced geothermal system (EGS). A major outcome is the creation of a modular, open-source Python framework for play fairway analysis (PFA) in 2D and 3D, called geoPFA. The PFA workflow has been expanded to produce pseudo conceptual models, and will soon be refined to assess reservoir components through integration with the thermo-hydraulic-mechanical-chemical (THMC) simulator TReactMech, to enable iterative coupling between PFA and THMC models, improving characterization of superhot systems. All three of the Icelandic Deep Drilling Project's production scenarios were analyzed via this framework: (1) a superhot deep injection well paired with conventional production wells at Nesjavellir, (2) a superhot deep production well at Nesjavellir, and (3) superhot enhanced geothermal system at Newberry Volcano. This analysis provides useful insights around conceptual modeling of these production scenarios, helping to inform decisions around which scenario is best suited for which types of environments.
The DEEPEN (DE-risking Exploration for geothermal Plays in magmatic ENvironments) project was a multi-laboratory, international effort to reduce uncertainty and improve resource characterization in superhot geothermal systems. Building on this foundation, this work advances open-source tools designed to lower the exploration risk and cost of superhot geothermal projects while promoting transparency, reproducibility, and efficiency in exploration workflows. These tools are being tested at two key sites: (1) the Nesjavellir Geothermal Area in Iceland, where the Icelandic Deep Drilling Project (IDDP) will drill its third well, and (2) Newberry Volcano in Oregon, USA, where Mazama Energy will pilot the first superhot enhanced geothermal system (EGS). A major outcome is the creation of a modular, open-source Python framework for play fairway analysis (PFA) in 2D and 3D, called geoPFA. The PFA workflow has been expanded to produce pseudo conceptual models, and will soon be refined to assess reservoir components through integration with the thermo-hydraulic-mechanical-chemical (THMC) simulator TReactMech, to enable iterative coupling between PFA and THMC models, improving characterization of superhot systems. All three of the Icelandic Deep Drilling Project's production scenarios were analyzed via this framework: (1) a superhot deep injection well paired with conventional production wells at Nesjavellir, (2) a superhot deep production well at Nesjavellir, and (3) superhot enhanced geothermal system at Newberry Volcano. This analysis provides useful insights around conceptual modeling of these production scenarios, helping to inform decisions around which scenario is best suited for which types of environments.
Studies on domain adaptation (DA) for remote sensing (RS) imagery analysis lack consistency in selection and description of evaluation scenarios. Without properly characterizing datasets, model assumptions, and evaluation scenarios, it is difficult to objectively compare DA methods and reach conclusions about their suitability across different applications. With this motivation, this work seeks to empirically assess to which extent the interaction between data characteristics and model assumptions influences the effectiveness of DA methods. Using the widely explored task of building footprint segmentation as a case study, we perform a large-scale study across over 200 DA scenarios that include variations across view angles, areas observed, and sensors used for data acquisition. Rather than adopting different model architectures or optimization criteria, we contrast the performances of two DA methods based on adversarial learning that differ only in their assumptions about source and target domains. Informed by metadata and data characteristics unveiled using traditional computer vision (CV) techniques as well as pretrained deep models, we provide a detailed meta-analysis of experiments highlighting the importance of accurately considering data assumptions for DA in RS segmentation tasks. As demonstrated by a “cherry-picking” exercise, different claims regarding which model is best could be made by selecting different subsets of evaluation scenarios. While well-calibrated assumptions can be beneficial, mismatching assumptions can lead to negative biases in DA applications. Furthermore, this study intends to motivate the community toward more consistent evaluation protocols while providing recommendations and insights toward creating novel benchmark datasets, documenting data characteristics, application-specific knowledge, and model assumptions.
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Silica is a primary component of rocky planet interiors and its melt properties are important for understanding planetary formation and differentiation, magma oceans, and the deep mantle. Although well understood in the solid state, the high-pressure behavior of liquid silica is poorly constrained at lower mantle pressures. Using laser interferometry to measure shock wave profiles, we report measured stress-density states and longitudinal sound speeds in shock-synthesized stishovite, from fused silica staring material, and across the solid-liquid phase boundary up to 154 GPa. Our results constrain completion of melt at 80 GPa and show that at pressures relevant to the deep mantles of Earth-sized, rocky planets, the Grüneisen parameter for liquid silica increases with compression. This finding is consistent with a continuous increase in Si-O coordination above six for liquid silica at core-mantle boundary relevant pressures and temperatures.
Recognition modes of individual T cell receptors (TCRs) are well studied, but factors driving the selection of TCR repertoires from primary through persistent human virus infections are less well understood. Using deep sequencing, we demonstrate a high degree of diversity of Epstein-Barr virus (EBV)-specific clonotypes in acute infectious mononucleosis (AIM). Only 9% of unique clonotypes detected in AIM persisted into convalescence; the majority (91%) of unique clonotypes detected in AIM were not detected in convalescence and were seeming replaced by equally diverse “de novo” clonotypes. The persistent clonotypes had a greater probability of being generated than nonpersistent clonotypes due to convergence recombination of multiple nucleotide sequences to encode the same amino acid sequence, as well as the use of shorter complementarity-determining regions 3 (CDR3s) with fewer nucleotide additions (i.e., sequences closer to germ line). Moreover, the two most immunodominant HLA-A2-restricted EBV epitopes, BRLF1 109 and BMLF1 280 , show highly distinct antigen-specific public (i.e., shared between individuals) features. In fact, TCRα CDR3 motifs played a dominant role, while TCRβ played a minimal role, in the selection of TCR repertoire to an immunodominant EBV epitope, BRLF1. This contrasts with the majority of previously reported repertoires, which appear to be selected either on TCRβ CDR3 interactions with peptide/major histocompatibility complex (MHC) or in combination with TCRα CDR3. Understanding of how TCR-peptide-MHC complex interactions drive repertoire selection can be used to develop optimal strategies for vaccine design or generation of appropriate adoptive immunotherapies for viral infections in transplant settings or for cancer.
We report the results of an extensive set of simulations exploring the sensitivity of the BlackCAT CubeSat to long-duration gamma-ray bursts (GRBs). BlackCAT is a NASA APRA-funded CubeSat mission for the detection and real-time subarcminute localization of high-redshift (z ≳ 3.5) GRBs. Thanks to their luminous and long-lived afterglow emissions, GRBs are uniquely valuable probes of high-redshift star-forming galaxies and the intergalactic medium. In addition, each detected GRB with a known redshift serves to localize a region of high-redshift star formation in three dimensions, enabling deep follow-on searches for host galaxies and associated local and large-scale structures. We explore two distinct models for the GRB redshift distribution and luminosity function, both consistent with Swift observations. We find that, for either model, BlackCAT is expected to detect a mean of 42 bursts per year on orbit, with 6.7% to 10% of these at z > 3.5. BlackCAT bursts will be localized to an r 90 ≲ 55'' precision and reported to the community within seconds. Due to the mission orbit and pointing scheme, bursts will be located in the night sky and well placed for deep multiwavelength follow-up observations. BlackCAT is on schedule to achieve launch readiness in 2025.
Ultrawide-band-gap (UWBG) semiconductors are promising for fast, compact, and energy-efficient power-electronics devices. Their wider band gaps result in higher breakdown electric fields that enable high-power switching with a lower energy loss. Yet, the leading UWBG semiconductors suffer from intrinsic materials' limitations with regard to their doping asymmetry that impedes their adoption in CMOS technology. Improvements in the ambipolar doping of UWBG materials will enable a wider range of applications in power electronics as well as deep-UV optoelectronics. These advances can be accomplished through theoretical insights on the limitations of current UWBG materials coupled with the computational prediction and experimental demonstration of alternative UWBG semiconductor materials with improved doping and transport properties. As an example, we discuss the case of rutile GeO2 (r-GeO 2 ), a water-insoluble GeO 2 polytype, which is theoretically predicted to combine an ultra-wide gap with ambipolar dopability, high carrier mobilities, and a higher thermal conductivity than β-Ga 2 O 3 . The subsequent realization of single-crystalline r-GeO 2 thin films by molecular beam epitaxy provides the opportunity to realize r-GeO 2 for electronic applications. Future efforts toward the predictive discovery and design of new UWBG semiconductors include advances in first-principles theory and high-performance computing software, as well as the demonstration of controlled doping in high-quality thin films with lower dislocation densities and optimized film properties.
Here, we report on adding an important dimension to the fundamental understanding of the evolution of the thin film micro structure evolution. Thin films have gained broad attention in their applications for electro-optical devices, solar-cell technology, as well storage devices. Deep insights into fundamental functionalities can be realized via studying crystallization microstructure and formation processes of polycrystalline or epitaxial thin films. Besides the fundamental aspects, it is industrially important to minimize cost which intrinsically requires lower energy consumption at increasing performance which requires new approaches to thin film growth in general. Here, we present a state of the art sputtering technique that allows for time-resolved in situ studies of such thin film growth with a special focus on the crystallization via small angle scattering and X-ray diffraction. Focusing on the crystallization of the example material of BaTiO 3 , we demonstrate how a prototypical thin film forms and how detailed all phases of the structural evolution can be identified. The technique is shaped to enable a versatile approach for understanding and ultimately controlling a broad variety of growth processes, and more over it demonstrate how to in situ investigate the influence of single high temperature sputtering parameters on the film quality. It is shown that the whole evolution from nucleation, diffusion adsorption and grain growth to the crystallization can be observed during all stages of thin film growth as well as quantitatively as qualitatively. This can be used to optimize thin-film quality, efficiency and performance.